The land carrying capacity and environmental risk assessment of livestock and poultry breeding considering crop planting

Environmental Science and Pollution Research - At present, the contradiction between survival and ecology necessitates the integration of crop planting, chemical fertilizer application, and...     

生态足迹影响因子的定量分析

人口的膨胀和人类工业化进程的加剧，使得人类向自然界获取越来越多的资源，同时向环境源源不断的输入废弃物，已经严重超过了自然生态系统的供给能力和环境容量，生态环境日益恶化，水土流失、草场退化、植被消亡、生物多样性锐减、全球变暖等情况已经严重到难以遏制的地步，危及到人类自身的生存。在和平与发展成为世界两大主题的今天，人们越来越关注可持续发展的问题。在现有资料的基础上，利用主成分分析方法，定量地讨论了中国各省（区市）1999年生态足迹大小与其影响因子间的关系。结果表明，生态足迹的大小是各省（区市）的大中型企业个数、全社会固定生产投资等众多因子共同作用的结果，其中总人口和GDP是生态足迹大小的主要影响因子，其因子载荷量分别达到了0.940和0.913。值得一提的是非农业人口与生态足迹的相关系数超过了农业人口，这说明由于消费模式和生活水平等的差异，非农业人口对生态足迹的影响大于农业人口对生态足迹的影响。在此基础上建立了生态足迹影响因子的多元线性回归模型，以期为生态足迹在进行区域可持续发展评价的方法上提供新的思路。     

构筑防灾科技学院学生文化体系

文章从四个方面阐释学生文化体系的构成，即重在打基础，鼓励学生个性化发展；培植防灾学院自己的学生文化“精英”；创新与创业是学生文化的闪光点；培育一个好的学生文化的管理环境。做到此四点，就可办一个好的、有特点的、合格的高等学校。本文有的放矢，用事实来说话。     

水库沉积物贫营养型好氧反硝化菌(Pseudomonas sp.)分离及其对微污染水体脱氮效率分析

为探究深水水库沉积物微生物功能特征及利用价值,于2019年在实验室对小湾水库表层沉积物微生物进行了驯化分离,并分析了其中一株细菌的脱氮效率.结果表明,分离出的细菌XW731经鉴定属于假单胞菌属（Pseudomonas sp.）,是一种贫营养型好氧反硝化菌;在分别以NH₄⁺-N、NO₃^--N和NO₂^--N为唯一氮源时,该菌对NH₄⁺-N、NO₃^--N和NO₂^--N去除率分别为33.6%、68.5%和9.1%;以NH₄⁺-N和NO₃^--N为氮源时,对NH₄⁺-N和NO₃^--N去除率分别为66.4%、89.6%,同步硝化反硝化能力更强.将该菌投加到两种城市微污染水体后测试表明,该菌对城市河道水体的NH₄⁺-N和NO₃^--N去除率分别为38.3%和42.4%,对城市降雨水体的NH₄⁺-N和NO₃^--N去除率分别为22.2%和7.7%.     

Performance of different macrophytes in the decontamination of and electricity generation from swine wastewater via an integrated constructed wetland-microbial fuel cell process

Plants constitute a major element of constructed wetlands(CWs).In this study,a coupled system comprising an integrated vertical flow CW(IVCW) and a microbial fuel cell(MFC) for swine wastewater tre atment was developed to research the effects of macrophytes commonly employed in CWs,Canna indica,Acorus calamus,and Ipomoea aquatica,on decontamination and electricity production in the system.Because of the different root types and amounts of oxygen released by the roots,the rates of chemical oxygen demand(COD) and ammonium nitrogen(NH_4~+-N) removal from the swine wastewater differed as well.In the unplanted,Canna indica,Acorus calamus,and Ipomoea aquatica systems,the COD removal rates were 80.20%,88.07%,84.70%,and 82.20%,respectively,and the NH_4~+-N removal rates were 49.96%,75.02%,70.25%,and 68.47%,respectively.The decontamination capability of the Canna indica system was better than those of the other systems.The average output voltages were 520±42,715±20,660±27,and 752±26 mV for the unplanted,Canna indica,Acorus calamus,and Ipomoea aquatica systems,respectively,and the maximum power densities were 0.2230,0.4136,0.3614,and0.4964 W/m~3,respectively.Ipomoea aquatica had the largest effect on bioelectricity generation promotion.In addition,electrochemically active bacteria,Geobacter and Desulfuromonas,were detected in the anodic biofilm by high-throughput sequencing analysis,and Comamonas(Proteobacteria),which is widely found in MFCs,was also detected in the anodic biofilm.These results confirmed the important role of plants in IVCW-MFCs.     

Effects of Na+ on Cu/SAPO-34 for ammonia selective catalytic reduction

Copper-exchanged chabazite (Cu/CHA) catalysts have been found to be affected by alkali metal and alkaline earth ions. However, the effects of Na⁺ ions on Cu/SAPO-34 for ammonia selective catalytic reduction (NH₃-SCR) are still unclear. In order to investigate the mechanism, five samples with various Na contents were synthesized and characterized. It was observed that the introduced Na⁺ ion-exchanges with H⁺ and Cu^2 + of Cu/SAPO-34. The exchange of H⁺ is easier than that of isolated Cu^2 +. The exchanged Cu^2 + ions aggregate and form “CuAl₂O₄-like” species. The NH₃-SCR activity of Cu/SAPO-34 decreases with increasing Na content, and the loss of isolated Cu^2 + and acid sites is responsible for the activity loss.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Reduction of SO2 emissions by ammonia gas during unstaged combustion

The reduction of SO₂ by the addition of ammonia gas has been studied in a 2 m high fluidized bed combustor having a 30 cm static bed height and a freeboard height of 170 cm. Ammonia gas was injected at 52 cm above the distributor where the temperature is ca. 700° C by an uncooled stainless steel tube injector. Experiments were carried out to investigate the effects of amminia gas injection on sulphur dioxide emissions at unstaged conditions of: (i) excess air level, (ii) NH₃:SO₂ molar ratio, (iii) fluidizing velocity and (iv) bed height.A maximum reduction of 75% in SO₂ emissions was found at 40% excess air, at an NH₃:SO₂ molar ratio of 5.4. The onset of SO₂ reduction occurred at an NH₃:SO₂ ratio of 1.5 However, the most effective ratio was found to be between 3 and 5. Fluidizing velocity and bed height were also found to have significant influence on SO₂ reduction.It is difficult to determine how the SO₂ reduction varied with operating conditions. When ammonia is added in the main combustor zone, the temperature is much higher than that required for the occurrence of sulphur dioxide-ammonia and sulphur trioxide-ammonia reactions. However, this paper points out the significance of ammonia addition in the reduction of sulphur dioxide.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Chemical monitoring and evaluation of the water quality of the Douro River at the Crestuma-Lever dam

Water taken from the Crestuma-Lever dam on the Douro River was evaluated considering the Portuguese Decree-law no. 236/98 of 1st August, 1998. This paper presents the metrological characteristics, validation of analytical methodologies and the data obtained from seasonal samplings. The test parameters selected are: temperature, pH, chloride, conductivity, carbon dioxide, alkalinity, hardness, oxidability, dissolved oxygen, BOD5, total suspended solids, sulfide, phosphate, silica, ammonium, nitrite, nitrate, sulfate, sodium, potassium, calcium, magnesium, iron, manganese, copper and zinc. From data analysis we can conclude that the water presented the following characteristics: a pH situated in the neutral zone; a median mineralization; a medium hardness; well oxygenated water; and a carbonate hardness exclusively due to bicarbonates. The temperature, pH, dissolved oxygen, ammonia, chlorides, sulfates, copper, zinc and BOD5 were compared with the legal limits presented in annexes XXI and I of Decree-law 236/98. The results relating to total hardness, nitrites, calcium, magnesium, sodium, potassium, aluminium, sulfides and oxidability were compared with the legal values in annex VI as there are no limits relating to these parameters in annexes XXI and I.